{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPI7G4iufTNp722WorsFQqy"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#1. Identificação de Sistemas via LSTM RNN para Sistema de Aquecedor SISO"
      ],
      "metadata": {
        "id": "XBsBx8knzYye"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import required packages\n",
        "import numpy as np, pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "zp1gDGs_zYOy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# read data\n",
        "data = pd.read_csv('TCLab_train_data.txt')\n",
        "heaterPower = data[['Q1']].values\n",
        "temperature = data[['T1']].values\n",
        "\n",
        "# plot data\n",
        "plt.plot(temperature, 'k')\n",
        "plt.ylabel('Temperature')\n",
        "plt.xlabel('Time (sec)')\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(heaterPower)\n",
        "plt.ylabel('Heater Power')\n",
        "plt.xlabel('Time (sec)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 899
        },
        "id": "X-k2g8pPzxZx",
        "outputId": "0c590df8-0769-45b7-d00c-8271de8deacc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Time (sec)')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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f5GpUh4kMSeGdCX2x0U8S0zQ+Bld1amFiRPqxcoyMXr9s1gFdTe3Znpjh3VvhmuyWeH/jAazfe0J530q8BJ2MwKQldExkSAqZzeKR2cz8e70Y6dyVWJaGoSujx4bUzb5b62SM7NUGG388gfV7T0i3DIWQrLdeqmBIL3bpSbUaBx2Q41nVCMrQtiiJhwSxBOPC3qufT8+ZH4p0ePWbw1hwilC2AwK9sEeGHEuGRAJwRiNs9rKs+7mIn//j1bkGnrVyF+at3Yv4mCi8eOtlF9yfyK5Xezm1oSLyhz0yZDjbVq4hxq10C0tQftu0x+ctK6MuYf9g80H8dKoa+09UYPl3Fw4uJjKL39O1Ou24ttn/NWIiQ2QQGY7m9c6hzMrJ6pady6hTSxpmKMHqJJto6IaKqr5v26M/azCRIdPYtSHQGrcy2FeGLhmbOP9heS5JR0zLFY19961wxnWmH46RITKYHHf2Da3atOrqiXPJIEmWyxlKCIHDpZWqeiaS4qKRFBttYFTmOZfQ6z1fZ25ETGSIDOKSZ4iMboxuTOsaLuXUkgk3FbTb+vGXlNqtLI2Z+s9t+HDLIVXfiYmKwL8n9keXVkkGRWUMp6wzKzGRIcezrqKQp/NYnki0MftI8vxTXGSurQdKAADRkS5EBNGbWF3rRfUZLwqLymyXyJhBhvF6RmIiQ2SQc1fc8M6+wbrgLjIGXbWkZXb1l8HZdSpP6+DUhuofd/dDn3bNAk43du56fLnzJxMi0o/fnjWdTgmHCw72JcNZdV5WlqNpZ3Udm1uaunXoddZC1CScFkE4XbUTRkU1DBMZokaEmoDJcMHN+WNOZHf+kWiEMs7IuIVot4bEJqsyJMqpvXAorB9hXvygMZEh04RbpeSkbmGzStLYqaWw6o4IgVN6Ms4Vwzn70Pn8lcyoteiQzeMCTGSIAtCaj8jUBtstp6oLV7lqybpQyALKDeVstt3WsesjLuyKiQyR0Rx0GGT65dfnvW+2+g1R/TFXsq1Rp7WXWp91asf7pPiLWK9ESJbxgkZhIkNkECfeR8Z0Rl21FGCGMuaeTjltFIxwv2onnNa1HpjIkPEs3ietqhNkqIPPHdmGeGdfkwpz/h1NDXvWks9v2qvRkGG7MouTixpO69FoTGTIsayuKJRG2F7tpF9mF8Wo+8g4lVMWk92utjOK/o8ocCYmMmQau3UT69V4ynDEb5tFf94pBT54MzyF052VeRopdExkiIwiQW+CXgmAVc1J3e3ppajrfe7sa/7P+x8U6qwGn1f9kBpMZIgC0labsg5W7/zLbk15zIMMSZIK4dC4h0MPXDDrUa8E1enbDBMZIoNZe2dfuecXiFH34gl2fg6v/6WltUdGip47HTilHGZhIkNkEN7MTb0L7h9i2akle641pzSAdh8jY/f47YaJDBnO+rrVqodWyiPkLmqznn593qqSabCvz9OvJYjHydT2yNhxjJC/JEc5xarzbzol0T0fExlyLKurNlPGdwRg9o3kdFN31ZIJA6ZtU7f7uSeQ1du6/nj5NQWPiQyZhnUSBXL+kagZN8Qj+eh1I0c78Juk6/aIAmeLsjoAO5v6/las+uFYyPMZckkaZo7qrkNEpKdQG08ZboinVxe1VRVh3RH5vwoOYsn2IuV9d1QE/nBDVwzpmq5pvk7tYndKyqeMMXF6C9wI526fxmAio1FlTS0WbTmky7wWbDzARMaBnFgJG12/nj82Ijs9EQBQfcaLE2eqfab9+JsjmhOZYDlxHdqBcmdfi+MwErct/ViayDz11FP4wx/+4PNe586d8f333wMAKisr8eCDD2LBggWoqqrC0KFD8eqrryItLc2KcBu1eGJ/NImJVP29kxU1uOW1fAMiIj1prXBkHahqJ0O6pmPdtGtQVlmjvPevzQfx2uo9hv5uQ0fErgCfG83fdmTX9dsYrT0yTuvJCIdTa3qwvEema9eu+Pzzz5XXUVHnQpoyZQo+/vhjLFy4EMnJybjvvvswcuRIrF271opQG3VxywQkuNUvyp9OVSn/F0LYcuQ9BeaE+8iY/9DIc7+XnhyL9ORY5XWLBLe+v2mz1i8cqolzq8T5hW0oQTVui7TXth4syxOZqKgopKdf2D1cWlqKuXPn4r333sPgwYMBAPPmzUOXLl2wbt069OvXz+xQdXf+EZ5TKyi7NRS6+Xl91tR6cfBkhc9HyXHRSIyNtiAoIvsI+vJrY8PQzKl1umwsT2R27tyJjIwMxMbGIicnB3l5ecjKykJBQQFqamqQm5urTJudnY2srCzk5+c3mshUVVWhqupcT4fH4zG8DFrVP8oN06beFFblUXW9Cj8er8CA51f6fOaOisAnDwzERS0SDI1B7xtzGb0s+Yyd0DjlmMH2Bz86xa/XfuD0/cnSy6/79u2Lt956C0uWLMHs2bOxd+9eDBw4EGVlZSgqKkJMTAxSUlJ8vpOWloaioqKGZwggLy8PycnJyl9mZqbBpdDO4dvWhUwusNU7b9fWSejYMgHuqAifP5cLqDrjxc7iMmsDlFLwDUAobYUM45bUsnvbrsa5BNz5Glqv4bSu9WBpj8ywYcOU/3fv3h19+/ZF27Zt8c9//hNxcXGa5jlt2jRMnTpVee3xeKRNZnzuFCoEwmO3tY9QK5Ok2Ggsmzrogvd/PfsrbNp3MrSZq2STG/ta8nvBrmdZxrA1FIXjBoUqPXMOKxcZQqob4qWkpKBTp07YtWsX0tPTUV1djZKSEp9piouLGxxTU8ftdiMpKcnnzwi63zFV39kRnaX7dmrslhrMfmVm4+a4BMEmwqFHJpjtmI8oCI5UicypU6ewe/dutGrVCr1790Z0dDSWL1+ufF5YWIj9+/cjJyfHwij1U7+SdOoG5gROrkxlZfWBOHdHayn3kVF7+bUBsVjBjqc+rWTpqaWHHnoII0aMQNu2bXH48GFMnz4dkZGRuP3225GcnIwJEyZg6tSpaNasGZKSkjBp0iTk5ORId8WS5jqXD6Ejg+l2Z1+zHhpZ93tMHzVxSi0STtuBGQexTj9FZ2kic/DgQdx+++04fvw4WrRogQEDBmDdunVo0aIFAODFF19EREQERo0a5XNDPKfwHSNjXRxGc3LZwo5E61KvULTMx5Ib4oXRffvVP/3auFiswKv31LE0kVmwYIHfz2NjYzFr1izMmjXLpIjMxW3UHBK1vZaxS4WoXHZrcLxaEhGpF6HUwakXDj3UDltllpJqjAw5m9ndxLJ3S5txVK/bnX0lWpbyREJGcUqPhN3jtwvLb4gXznxuiOf8AxDbCYejQtlId7WKNIHI7/ipKizYeAAV1WdCntcZb91gX/NWgNcr8I+N+3G45HTAaa/pkoZeWU0b/VxNzeH3GVo6b4BObWeYyFjI5xEFbDTDhplHaee2Kp3u7KvLXOTS8A3J5CtpMBFZGfa8tT/iryt36TY/lwuIi1b/MF6tNu8/icc/3B7UtB99fQSrH/6FwRFRsJjIaKRH4hEug32JgnXulIJ8XSE++6uFKZ18S+asuieU92iTjF5tG++tCFb3Nslo1iRG1XdCSUDLKs/2JKU2icENl2U0OM3J8mos3noYpypD73UKZhOXcDeQEhMZC/ncR8bCOMg/J1QmId/Z1+TLr4Oaltm/QqZNdFCnFpg6pLPVYWiWkRKH6SO6NvhZYVEZFm89rOvv2aVHUGYc7KsDrZW8ExrIYHCXtI7eFaJZ9au/XUOP/YbthP6sXKRhUpVSI5jISIIZOJH2O7rSWRxrZx/BDOTVez9w6vbBREYSzty8fJneOP38e7ImiWZEJd1VQBLSUrlbc0M8uZO8cLphn9EkrbKkxUTGQhzsS9QwWdpCl8//JQmqAbIsLztTk9DqWV2bUfU7fftgImMhn4qRiYx0jEourWgQ9boKSKauaSMjkaeU9uPwNlOV0Pd1Ls1gMJGxkNOzZLKe3Xr6zj2hgDuH3ciQ5OoRgb96Odg6O5j9zt+8rF+S9sJERiM9GgjeEM8e2KjKdV8XvSOxS7IXzHgnu5SFSE9MZCTh5ArIyWWTnd6DfY1el3UJvUR5U4O4SV/IyucjyZRoq+ZnY9L9qiWHbrhMZHSg9Yjd51lLegVDRIaQua1kr6GzODXhMAoTGQv5nFoKgy3XqqpW1iVr5ioP+c6++oQRkGy7ga2P9C1i16RKzbanR31t5lKy6zoJFh9RYCHfZ7eQ3py96wZJ7zv7qpz+TK0XRZ7KoKevrKk1LhgyVDisDiPqlKoztTh4ssLnPc/Pz62yQx12uroWx8urkBIfgwS3NSkFExkL8WhPboZVzGG02m9+LR9b9peo/p6/fcOM/Ua2niEguAdqShi2uXS+CMMM2w6WYsDzK03+VX14Kmsw6IWVOFlRg+d+1Q3/r2+WJXEwkZGEjBUn2Z9eN1vV+v2tB0oAADFREUE3EGlJseitw9OT/Qn11EA4nApWy8rBvnbUq21TtE2NR1Fpwz2WcTGRGJzdUtff1HurPXCiAicrzvYeRVo4UIWJjEZ6bxC8/FperJhDt/bRwWiR6LY6DMfiNiopP+slLSkWqx/+hXmxGCgtyY1bL7emNwbgYF/LKRWQg/MYJmnW02uwn9qeCHZchB8rcio9ftPJ26rTE11ViUxNTQ3uuusu7N2716h4bCmUjSQM8hiykCyVsxEVqV4Jsr/kTK76/+d77FgcReMk2dhMEKikPPVoLlWJTHR0ND744AOjYglLdQP3wmG7d/pRgVp26qnSsu7qV+Z6rnoZtiNZ1xwbUGPJsO3JRJbNTfWppZtuugmLFy82IJTwxP3COOGUJDZGSZZ02tDULMr6y91uV+g1VE6bFcF0jhnsa/sCmM/q+9SoHuzbsWNHzJgxA2vXrkXv3r3RpEkTn8/vv/9+3YILB3X7jJ2Ozik04VJN1t+iZSuz0/Y2tr1nsR71z6kHdaoTmblz5yIlJQUFBQUoKCjw+czlcjGR0cipG5itcaUotBxx+ZxakrihtdtalnlZAvbrfSP7U53IcKCvvs42EAJzVu9GYqz2q+F7t22Kwdlp+gVGUhFCYOGmg9h3olzV94pKqwDo2COi5jbu9f5vRNdzuOWZspfXyvj0yJ20nja1A6enlppbzurqauzduxcXXXQRoqLC73Y0eg2qa+KORHWFF2/n7wtpPjGREfj6qSGIjY7UJS492W2nP58MB5jfF5XhkQ++1vz9JhbcOlwYdG5JgtVh+22ayElU124VFRWYNGkS5s+fDwD44Ycf0KFDB0yaNAmtW7fGY489pnuQTvbSbT2xqvCo5u97vQLz8/ehutaLqjNeKRMZCl1Z5RkAQFJsFEb1bqPqu61T4tArK7Q75Wq6agn2OLXkT/24rR7QKDunjE/xv5bVbQPhssVYvX+rTmSmTZuGbdu2YdWqVbjuuuuU93Nzc/HUU08xkVFpUKcWGNSphebv19R6MT/E3hyzsCHwpeWovnmiG9NHdNU/GAP4XLVkXRgB2a13paH9iPsWBcMpyeb5VCcyixcvxvvvv49+/fr5DOrq2rUrdu/erWtwRKFg1a4/rRWhbANAAyUvMt6PRb6IGibZqraEXdaVU6hOZI4dO4aWLS98kFV5ebl0lRURIF+lEi67iV16ZJyksfzrwIkKzFzyvXKKsiGZTePw1A1dEa3x6X8y5H4yxCAjo+ocWZa36kSmT58++PjjjzFp0iQA54603njjDeTk5OgbHZGFJNlHpaClHjRsjIwMNzq02cbx762H8PHXRwJOd+NlrXFF+2YmRCQfNb1wMvbYWcnqAxXVicxzzz2HYcOGYceOHThz5gxefvll7NixA1999RVWr15tRIwULO5bjiVLxakmDN8eGaurutDJ3JMWKLbq2rMro//FqRjV68LB4i8sKUSRpxI1tV7NMdStbmvWtTm/KfM2EM5UJzIDBgzA1q1bMXPmTHTr1g2fffYZevXqhfz8fHTr1s2IGIks5YRG2Ao+V19LvAj9jfuRMm5N3WNny3hRiwSMbCCReW31HhR5KkMMzBmkXOfkl6abS1x00UV4/fXX9Y7FVuQ4Pra+S08NVhChs2wRalh5svQiOYUZi1OP3+B+LjGH7pKqR3XdcccdmDdvHvbs2WNEPLbEHZe0sGOdourUUr3/y7aPhHoZqqyXsTYW17nTPg3T5c64ci4SMpAs+4HqRCYmJgZ5eXm4+OKLkZmZiTFjxuCNN97Azp07jYiPiGzK6DEyslSiTsJlqi9ZruQ1+vS41eVUnci88cYb+OGHH3DgwAG88MILSEhIwJ/+9CdkZ2ejTRt1dxytb+bMmXC5XJg8ebLyXmVlJSZOnIjU1FQkJCRg1KhRKC4u1vwbFF6UJ4tLdqgYNmNu6icyEj+iINjNQ5a1FsIQmYANTii7Sl0SZOVyCmVPV/WspRB+h/Sn7YYBAJo2bYrU1FQ0bdoUKSkpiIqKQosW2u5Qu3HjRrz22mvo3r27z/tTpkzBf//7XyxcuBCrV6/G4cOHMXLkSK0hOx6PppzL6jUb8uXX+oUStozcv60+oraLYJeSZMdOjqc6kfnf//1fXHnllUhNTcVjjz2GyspKPPbYYygqKsKWLVtUB3Dq1CmMHj0ar7/+Opo2Pfc8mNLSUsydOxd//vOfMXjwYPTu3Rvz5s3DV199hXXr1qn+HbKObD0iwbJp2IbSukjYUFor2CRIj03eilUtc4+fTJxapam+amnmzJlo0aIFpk+fjpEjR6JTp04hBTBx4kQMHz4cubm5eOaZZ5T3CwoKUFNTg9zcXOW97OxsZGVlIT8/H/369WtwflVVVaiqqlJeezyekOKTHRuI8GKn9e2ERLCx04Cylk1rXLpsVZIuEzKOLPuB6kRmy5YtWL16NVatWoU//elPiImJwaBBg3D11Vfj6quvVpXYLFiwAJs3b8bGjRsv+KyoqAgxMTFISUnxeT8tLQ1FRUWNzjMvLw9/+MMfgo5BK1lWoJ3Ypwk+j20D14+2p1/X+75ukdSbfyjjOep9N9jZyJJEaloXyhgZ//O0a+8p+SfJpmsY1aeWevTogfvvvx+LFi3CsWPH8MknnyAmJgYTJ05Ely5dgp7PgQMH8MADD+Ddd99FbGys2jAaNW3aNJSWlip/Bw4c0G3eROFO623cde36N6FSlrE99xeTDEmWtXf2pXCmukdGCIEtW7Zg1apVWLVqFdasWQOPx4Pu3btj0KBBQc+noKAAR48eRa9evZT3amtr8cUXX+Cvf/0rli5diurqapSUlPj0yhQXFyM9Pb3R+brdbrjdbrXFCoksO66MlS81zulHv773kZFjH6GGKT0y1oZhKVW7YzgvKAmpTmSaNWuGU6dOoUePHhg0aBDuvvtuDBw48IJTQIFcc801+Oabb3zeGz9+PLKzs/Hoo48iMzMT0dHRWL58OUaNGgUAKCwsxP79+/lwSgqKk9pOq3OeUC75dSK7FS3o3hKb39nX6G1ObULuoCqoQbLsB6oTmXfeeQcDBw5EUlJSSD+cmJiISy+91Oe9Jk2aIDU1VXl/woQJmDp1Kpo1a4akpCRMmjQJOTk5jQ70JbIDJyVY/ij3FQmT8spA+2Df0FeSU3oYndx7aNQ6snqRqU5khg8frvz/4MGDABDSjfD8efHFFxEREYFRo0ahqqoKQ4cOxauvvmrIb9mVHXY5u1ZvMlbMVq9vVUukboCpEYHoyP/Yk3r/Nz6UoGhJOoIe7GvTvVWWdUPWUD3Y1+v1YsaMGUhOTkbbtm3Rtm1bpKSk4Omnn4bXq/0R8ACwatUqvPTSS8rr2NhYzJo1CydOnEB5eTkWLVrkd3wMEclDOZ2h8+FaXUNu1l1cZeEvZhkacjsuU6NwWZhLdY/M448/jrlz52LmzJno378/AGDNmjV46qmnUFlZiWeffVb3IMkhrO5/1MieUetLSzIibNIjEw4CPT6g7n0JOyGJAlKdyMyfPx9vvPEGbrjhBuW97t27o3Xr1vjd734XPomMhDu8hCGRwyzecghfHywJatryqloA9sxfA51i+euKXYiLCdyh3SurKa7pkqZXWAGFWgfokcjYd4xJ4MIrCZ+xgdiGLKffVScyJ06cQHZ29gXvZ2dn48SJE7oERUS+rB67EB8TCQD4dHvjN6NsTBO36mrGZMEtW5cLiIuOxOmaWry5dm9Q34mOdGHb9CGIj9FvGWjKEwKMkdEj25SkTSM/jFpFVueuqveuHj164K9//SteeeUVn/f/+te/okePHroFZidWrkSrNyCZyXJ/Hyd4fHgXfFBwEGe86qvCQZ20PUxWNi6XC7NG98SXO38KOK0QwFtf/YiaWoGqGi/iY0L/fX9Hv6HWA07padAj4WetYT+qE5kXXngBw4cPx+eff67czyU/Px8HDhzAJ598onuARKGS7UjRjslndnoSHh9+idVhAKh/O33zf3twdhoGZwc+VeT1Crz11Y8A5EgOjBp43eBvGPYLpJV9T/cFR/VVS4MGDcIPP/yAkSNHoqSkBCUlJRg5ciQKCwsxcOBAI2Ikm5MtkbAzh9dHpuE26cvuz1qSdb+QNS69yLK1qOqR+fHHH7Fs2TJUV1fjtttuu+CGdkROIstOSsYyou2u34DpnRz4axwb+6269wO1q3pEatfG26Y5HEFFIrNy5Upcf/31OH369NkvRkXhzTffxJgxYwwLjtSR/WjKpvUbhSE9dyW598qz9Ng3Za9/9GD3niujWD0eMehTS0888QSuvfZaHDp0CMePH8fdd9+NRx55xMjYpGb1VSRkHqefXyb9mb3NBOxpCTCApS5ets/69CjJmuhIGlbIgk5ktm/fjueeew6tWrVC06ZN8cc//hFHjx7F8ePHjYyPAmAja1982q42dtvindp4nI+DfckqQScyHo8HzZs3V17Hx8cjLi4OpaWlhgRmJ9xxiawQJhmCjho7BXDuXXsv03BJGtUyqo2SZXmrGuy7dOlSJCcnK6+9Xi+WL1+O7du3K+/Vv+MvkZVk7awK5Xyy1eeineNcDez/oZEhrCvX2XnrfRra3zbQ2C8FG0F439mXtLJ6latKZMaNG3fBe7/97W+V/7tcLtTW1oYeFTmK1eOJrP59Ij0YefSrS0Nk4W6mR4LPWsK+gk5kQn2yNRmPO6K+ZOk2JXty4ed90oTtKFAviPIAz8YG++rwRPFwYPflZNe4A1F9QzwirazufiQKFpNY9ZQnbNt8P9fz9K3NF4VtMJEhCkCGyojt6jl6P6LAqFOPyiXNus9X/XeUJKPRmf48HTc0RzIuuZRjg2EioxF3eAoVx+6Q7kLcpPjQRdLC6nXORIbIRuzebS+LYA9EQlncyhOlJeg5CjxGJnQyHNxJEAJZQFUiU1tbiy+++AIlJSUGhWNPvNyQ1ODmEl7M6HkLdZOy8onisgim7FxOclKVyERGRmLIkCE4efKkUfFQCGTduayOy+rfJ7kZtX3ImLAaeR+ic49BML/gMi5rGen96ARZ6lbVp5YuvfRS7Nmzx4hYiIgCssslsEqcJgaqtffHLsvUFEyKbEd1IvPMM8/goYcewkcffYQjR47A4/H4/JH57HI0Yre70spUqcty5BMuZByIHco2UHckHqiukPVhhxQao2teq4dXqLqzLwD88pe/BHD2UQT1gxdC8M6+RER16sZTmPFTIbYjujzxOdAl3kQGUZ3IrFy50og4iKRl1MEGD36tE26LPtCTqe3SqxuQZDuV1T0V4UJ1IjNo0CAj4rAduXYXItLK374cSjt07vJrnR8aKXnjKHl4jVJzOlHGU4/B0DtqWZaCpvvIfPnllxgzZgyuvPJKHDp0CADw97//HWvWrNE1OLuQZb+1685lFNkrfNKIq7VRAXOmRvYJPQYmW9kZwl3dWlYvftWJzAcffIChQ4ciLi4OmzdvRlVVFQCgtLQUzz33nO4Bkv0xvSIjyD4wVfdHKZiQZPBgyDk3B6zP6Qd1mq5amjNnDl5//XVER0cr7/fv3x+bN2/WNTgKjrM30dDJVqlowQbGXPbbZkKrBfQZ7FsXCWskMpfqRKawsBBXXXXVBe8nJyfzjr/kl90OCmQ/4id9GHZDPIMadC1zDfaKIm7ypIYs24vqRCY9PR27du264P01a9agQ4cOugRFRA1zehexWcxMUmWp7M3CTZTMpvqqpbvvvhsPPPAA3nzzTbhcLhw+fBj5+fl46KGH8MQTTxgRIwUrzCpMsxhVL4dbA6cXM9vJUHpV9G7Qg9lcGpsm4EMjXfYe7KvEEMp3w+BZS+v3nMAdb24IOF1qkxg8cf0laNYkJrgZW5y8qk5kHnvsMXi9XlxzzTWoqKjAVVddBbfbjYceegiTJk0yIkYp8bQDkbVk3wOVy6+lj/Qc+0RKarRIdAMAfjpVhS9+OBbUd/p1aIZbL88yMizdqE5kXC4XHn/8cTz88MPYtWsXTp06hUsuuQQJCQlGxEdBcLlcUh8iWBWarD3cPD0kFzslGkDDvSoBHz1QN10je4U+W6R1d/bVczySnrunLHv60K7pmDf+cpwsrw447by1P+KbQ6WoqbXPfqE6kbnrrrvw8ssvIzExEZdcconyfnl5OSZNmoQ333xT1wDtgO0SGU3iPJUaocfpGrOcO2Vig2BJtcgIF37RuWVQ0372bTG+OVQa1LSybC+qB/vOnz8fp0+fvuD906dP4+2339YlKHImq/I9ux1xk/WctsUEGiOjJx7YOYOd9oGge2Q8Hg+EEBBCoKysDLGxscpntbW1+OSTT9CyZXAZHxFpwzZCH6Y8yDGE3yqrrMHoN9bj4MlzB41VNYEfyKv1ALku1qf+8y3yPv1e0zzKKmu0/bgkgll0wZ4WDreDJ6vrpaATmZSUFLhcLrhcLnTq1OmCz10uF/7whz/oGhypE167DoWrcBhj9M3BUnx9sOHu/ez0JA1z9D9+pUurJKwsPIby6lqUVwdOmBoT4QIubpmo+ft24eS6Vtm9JDltFIygE5mVK1dCCIHBgwfjgw8+QLNmzZTPYmJi0LZtW2RkZKj68dmzZ2P27Nn48ccfAQBdu3bFk08+iWHDhgEAKisr8eCDD2LBggWoqqrC0KFD8eqrryItLU3V7xCR8+hWz/qZT0g5UwjjTuq+0b55E/xtbG/l/ajICLRLjW/spzR7eGhn3NInEzW13pDmkxIfo1whYwUbtb2ko6ATmbqnXu/duxeZmZmIiND0vEkfbdq0wcyZM9GxY0cIITB//nzceOON2LJlC7p27YopU6bg448/xsKFC5GcnIz77rsPI0eOxNq1a0P+bSdx/vGptYzqAWCdS4G4oyLQMS30Ho5g7iPTrnmTkH/HCcL9EQtqqjtZ6jDVVy21bdsWAFBRUYH9+/ejutr3cq7u3bsHPa8RI0b4vH722Wcxe/ZsrFu3Dm3atMHcuXPx3nvvYfDgwQCAefPmoUuXLli3bh369eunNnRdybIC7YFLK1RcgvYTyhgZrcJtbIYivHMPQ6jZkqw+3as6kTl27BjGjx+PTz/9tMHPa2u1nV+tra3FwoULUV5ejpycHBQUFKCmpga5ubnKNNnZ2cjKykJ+fn6jiUxVVZXyRG7g7CBlCk+yDqWQNCwyiJbTHXqfIjnXI8Otz1Rc3KZQfX5o8uTJKCkpwfr16xEXF4clS5Zg/vz56NixI/7zn/+oDuCbb75BQkIC3G437rnnHnz44Ye45JJLUFRUhJiYGKSkpPhMn5aWhqKiokbnl5eXh+TkZOUvMzNTdUxqsXIIDhdT6LgM9Wkb6icKDeYMOiQSZtYL3C5CF8xYpnBYzHY8taa6R2bFihX497//jT59+iAiIgJt27bFtddei6SkJOTl5WH48OGq5te5c2ds3boVpaWl+Ne//oVx48Zh9erVasNSTJs2DVOnTlVeezweU5IZGXCgW8O4XJxJ9tV6LrmQPVJSLQxWaTD1pix1q+pEpry8XLlfTNOmTXHs2DF06tQJ3bp1w+bNm1UHEBMTg4svvhgA0Lt3b2zcuBEvv/wybr31VlRXV6OkpMSnV6a4uBjp6emNzs/tdsPttm7UPDmHLDspWcd+x6YNC9uxMxqwd8t+VJ9a6ty5MwoLCwEAPXr0wGuvvYZDhw5hzpw5aNWqVcgBeb1eVFVVoXfv3oiOjsby5cuVzwoLC7F//37k5OSE/DtOwh2PSD5W3I4j3JNvWW6ZL0kY2mhoT6xuglT3yDzwwAM4cuQIAGD69Om47rrr8O677yImJgZvvfWWqnlNmzYNw4YNQ1ZWFsrKyvDee+9h1apVWLp0KZKTkzFhwgRMnToVzZo1Q1JSEiZNmoScnBzLr1ii8GLUTqqm0pWlgnYiGZet3j0oZj6igJxBxv2iMaoTmTFjxij/7927N/bt24fvv/8eWVlZaN68uap5HT16FHfccQeOHDmC5ORkdO/eHUuXLsW1114LAHjxxRcRERGBUaNG+dwQj+zFRvsD2YBdGmPloZFm/Jblx8TWCu/Sk+pEpk51dTX27t2Liy66CL169dI0j7lz5/r9PDY2FrNmzcKsWbM0zd9IbJzlJ2vlHkpDbJdG3AyhHDEG+q4sY0r0uvKprjSy7hN2EQ77n5r7H8myn6geI1NRUYEJEyYgPj4eXbt2xf79+wEAkyZNwsyZM3UPkIIny0bVGF6mTuHEho+soSDJXteazeqqXXWPzLRp07Bt2zasWrUK1113nfJ+bm4unnrqKTz22GO6BkhEZCR/TZIeFbSWRk9r8vPZjmIcKT19wfvbD519AKXVDY4d6LmM7NgDZscDTtWJzOLFi/H++++jX79+PgXu2rUrdu/erWtwFJyzOwuPEBrDJUNWMLM9iHdHAgC++OEYvvjhWKPTNYmJNCsksjk79SRqekRB3X1k6isvL7dlJkfUGJm6j+WJxHp2q2bMaBAeuKYjWiS6UX2m8adXp8TF4MaerY0PxkLcT8KT6kSmT58++PjjjzFp0iQA57qh3njjDd7fhUgFVrpOF3rGFewc2qY2wbRhXUL+PfLPjqeK1FJVQkkqMdWJzHPPPYdhw4Zhx44dOHPmDF5++WXs2LEDX331VUiPFiDnslMXZYMkqrvCoSI1gymXRP+8qmy//duAHmcD1KynQNM6YZWrevq1xfWS6kRmwIAB2Lp1K2bOnIlu3brhs88+Q69evZCfn49u3boZEaOUZDrtUIcVpi9ZT0FIGlbY+mr3cdzx5gaf9wqLyiyK5izuykTB03QfmYsuugivv/663rEQEZnuWFkVjpU1PEC2RaL257adux8H0xI7sbp3wWqyHgD6E3Qi4/F4gpouKSlJczB2JMVKlyEGIpPo2dA8ef0lSImPbvCz9KRY9MpqqttvaSFF/UJhKZgbTsqSogedyKSkpPg9DymEgMvlQm1trS6BEelGlr2NpPOL7JZo37yJIfPmGBkicwSdyKxcuVL5vxACv/zlL/HGG2+gdWtnX85H4UuqBkimWCQh1fohKRi9TajtIbNjj5qWkK0uZ9CJzKBBg3xeR0ZGol+/fujQoYPuQZE2rNdthivMOiYs+7pTYLe/vg7Rkf6fBnN991aYceOlyms7PXnYKdSMZeLakYvqZy0RqWX3wY4yDf6z+siHgtelVSIAoKzyDE6UV/v9W7DxgMXR2ht3C/3Y8ca2mp9+TUTa2LGicCoj18Trd/TBj8fL/Z7uOFpWhdFvrG/0c24q5uMyPyuYTkFZOg5DSmTCukKWZAUC9jkaMXtzsctyIXXsUu1ERUbg4paJfqdp4uaxJFGogt6LRo4c6fO6srIS99xzD5o08R3xv2jRIn0iI1NVVJ/t/g6Gy+VCq6RYRETYpEUhR7L7KUsfwu9LshlZeiq0sGOtHnQik5yc7PN6zJgxugdjR3Zc6ec7VlaFwf+3CmVVZ4L+Tm6Xlnhj3OUGRkX1OarRJrIpJ9T3wbJTnRN0IjNv3jwj4yAdaL3SYc+xU0oS447yP/5bCKC61outB0o1/ZYV7LRDkjnsskXINNDcDkJZr+qetWSXLchYstStPEFLig4tmmDFg1f7neb7Ig+ue+lL2Kcp0M7oEmqpBNisOYtdxvtQGLHhNsnLrx3AzMqw7ghRzyfFEllFlkRCliNbu5JlPZ5P0rCCoqbetvrCHyYypO5x7T9vr94wyk703kftXLmRScJn9yIKGRMZUqXuQiUt9azZ5/tlPUojfTghl+YYGJsJg9Wl9LpbHIcaTGQ0stNKDlZw+6j6U0sUOi7v8MRk3Dzcx9STZZkxkQmR1ecG69O6Uak7F1r3HUm2YCINZNt+JQuHAnDy6pKoSQsaExlSJcJlvx4ZO8UakB1rGZ3JdPBActEjQdVj+3LC4G1VB7jGhREUJjIOoNd59mB24Lop7L+bBmZ0AuSoBMumrB6j0tgu54SGkMgsTGRIVaWp5dQSq2QyAhNBqmN1j4CT2HFZMpEhVepOLXnDqBHRe8fmmRE6X2O7EzcVuVjdg2emYA5wZWkGmMiQQs0uqumutKbXAc6pdNj7QGQsI3axcDlosbqcTGQ0clTDoumqJWNCIQpEjzpTls23sbJw/5Kbk9eP1UmJFkxkSBU7XrXkJDasY4hshfvYWXaq45nIhEiGjV6vDDqY+Sg9MtIc0xJpJ8vRZ6OD52UJkKgBstyPiYkMqaLloZFW0xoqkzW5cf1QHd5bSD92HNDMRIYMf2iknZIeM3GxkA3bjLAWbL4UbnWe1XkkExlSBJOJnzu1FD6s3kmB8FreFH4NoQxkOU1iNRnqO7WYyDiIGfthKKeWbLh/GIRLIhR6VLSytVmShUNkq8SOiQxpemjk2e/JvaHb8cgiECeWiRrH1W0+7mPBk6UFsDSRycvLw+WXX47ExES0bNkSN910EwoLC32mqaysxMSJE5GamoqEhASMGjUKxcXFFkV8jkwDDfXa74LZgSPqTSR5HkMO54Ttz44DK2Um2zZhx/Vrx0TO0kRm9erVmDhxItatW4dly5ahpqYGQ4YMQXl5uTLNlClT8N///hcLFy7E6tWrcfjwYYwcOdLCqH3ZcaWHon5xJasziByD+5acwqm6V/f0a2uXTJSVP75kyRKf12+99RZatmyJgoICXHXVVSgtLcXcuXPx3nvvYfDgwQCAefPmoUuXLli3bh369etnRdiOo+WhkQBw8GQFIiMCb8AV1We0hKUb2U+BEXETDU04JRh0IUsTmfOVlpYCAJo1awYAKCgoQE1NDXJzc5VpsrOzkZWVhfz8/AYTmaqqKlRVVSmvPR6PwVGHl/r3axj0x1XWBWICoxsXNfNnMnaO1Ud/Zgq3Hl8rcQ+ro2Kjk2ShSTPY1+v1YvLkyejfvz8uvfRSAEBRURFiYmKQkpLiM21aWhqKiooanE9eXh6Sk5OVv8zMTKNDl4YZ43aSYqMwOLsl3FERqv7Sktzof3Fzw+MjCoYsY9yYqNiX0w8u7FQ6aXpkJk6ciO3bt2PNmjUhzWfatGmYOnWq8trj8YRVMqOFuquWXHjzzsuNC0ZCevcAhNJ4sd07x04VLdkH9zH1rE7IpUhk7rvvPnz00Uf44osv0KZNG+X99PR0VFdXo6SkxKdXpri4GOnp6Q3Oy+12w+12Gx2yVPS6PbfTbvPtrNKQEWTd5J1+tE/yUm56aqNN0NJTS0II3Hffffjwww+xYsUKtG/f3ufz3r17Izo6GsuXL1feKywsxP79+5GTk2N2uEREJDGjTxk67WDPKSztkZk4cSLee+89/Pvf/0ZiYqIy7iU5ORlxcXFITk7GhAkTMHXqVDRr1gxJSUmYNGkScnJyLL9iyU7ZaiAOKgqFASe1JYGK4qCihiU7bqtqQpZlrJmliczs2bMBAFdffbXP+/PmzcOdd94JAHjxxRcRERGBUaNGoaqqCkOHDsWrr75qcqSNk+kKCiclV+SLq9b5hBA84tdKj8WmcicTwp6JSrBkSVKCYWkiE8x54NjYWMyaNQuzZs0yIaLw5tR9Utbd0U4VhdMw6SfSj9VthzSXX5N2Vm9EZB4esdfjgGSksfXpgKLZVrjvY3YsPhMZ4hUSAei9Y9uwnnCscG+0iBpjp2aBiQwpnFans5FyJqeu1YYaDm7DcuHa8CVLssNEhoiIHCGUhlXPMWt27uWW6QKWYDGR0UjGzVRrTDKWhXzZuF4kPxprMri+5ef0VaSqfBb3HDKRIQV7sclOQjmCdnojFG7s2ItA+mEiEyoZ9h8ZYpCYrEe3WuLiqnauhjYHrm/z6bnM7XhwqMQcRAUlS93KRIZ4eNoIO5/ndjI9GwcbtjNkITsmJuGAiQwp2D1rDlaGVIfbAslGyyZp9WbMRIYoADY2ZD72BpqNHbC+7LQ4mMg4iNZTIU69Vb6z8g9nriM6h6cyQ2fmEuT6koelz1oiubDngexk8/4S9Hp6mabvVp/x6hyNMbhPmi/cl7mamzDKksoxkdFIpmw8zPc7CjMXt0xAVIQLZ7wCJ8qrNc+neYIbzZrE6BiZehyXpg/Zkg+JmgfN7FQGJjIhkmz/0cROG6yTaFnsslXYVri4ZSI2PJ6L46eqQppPRkocYqMjdYqKwgETz4ZZXS8xkSGFU3dRzXc8ZoInrWZNYizvTdFb/c2N2x5R8DjYl8hkPKojko8xuaN993U7XQTCRMZB7LPZ2Y19KyOSXIBNi0mvvFjfyjNWlIkMnevGtvpEp86cVBxJ6gsiqemznzio4tCgrt60U53DRMYB1FwuR0Ryqt9w2KgNIbI89WMiQ2QjPNVAdCHT9oow2P3sWMfwqiWN7NTtFohyZsnSKMKQkzYi0iRgZyp3SrLI7mOnsGT7Eb/TbD1QalI0/jGRCRHP6hAR2Z+exxV2PkSJijzbqC39thhLvy0O7jsR1p7cYSLjIDzAb4TmZ1AZg8kvNcROl7uSc+vbUb3a4PuiMlRUnQlq+ogIF+4e2MHgqPxjIkPKJXRsYOXl0DqTGuHURtIOwr0e7JyeiLfvusLqMFThYF8HCPcdrzF6LRcuXzIKNy19Gd2rpbYuYN1hDiYyRHbCitGxGuqF4eomCoyJDPG0BRHZGns+whsTGUcJLSVhXUAkBw78NR+XuX0xkQmRHW8eRHJgtUm8K7d9MfGRBxMZcjyt1Y1MV47IFAuZh3mO+bjI7YeJjAOEuuPVNZI8OjQHFzORMYxO+IPddXngYS4mMkQByJR3yBQLhY7rkyh0TGTIsTh+ieyKR/TqcF8Pb0xkHER75ffznX11i4SI1GLyYi0jlj/rVHMwkSEikhR7GuTFxFMeTGQ0kmkj5iBde1KzDfFST2firisfrhP7YSITIids9OeuWrI2DiIimfGgUU6WJjJffPEFRowYgYyMDLhcLixevNjncyEEnnzySbRq1QpxcXHIzc3Fzp07rQmWbEtr75lRvSChnC5gPepc9bc39r8RBc/SRKa8vBw9evTArFmzGvz8hRdewCuvvII5c+Zg/fr1aNKkCYYOHYrKykqTIyUiIidj8mhfUVb++LBhwzBs2LAGPxNC4KWXXsLvf/973HjjjQCAt99+G2lpaVi8eDFuu+02M0O1Bc13sP35X8cNLNSpOOwFIaME2ue47QXHiuV0qOQ03FEN9wV4KmtMjia8WZrI+LN3714UFRUhNzdXeS85ORl9+/ZFfn5+o4lMVVUVqqqqlNcej8fwWImIyBmCPaC75k+rDY6EgiXtYN+ioiIAQFpams/7aWlpymcNycvLQ3JysvKXmZlpaJwy4EGb88l0lRwZo/46FlzhUmoSE4ncLi3hjooI+JcSH43B2S2tDjksSNsjo9W0adMwdepU5bXH4wmLZCYU4ty5JTIRGysie3G5XHhj3OVWh0HnkbZHJj09HQBQXFzs835xcbHyWUPcbjeSkpJ8/ozA+3qQFRw3jinMBRrbwTEyRIFJ2yPTvn17pKenY/ny5bjssssAnO1dWb9+Pe69915rg6uH9Yz8Sk/XYMn2I6q/t/PoKQOiATcaIoMUFpVp2tcBYMfhUp2jIbNYmsicOnUKu3btUl7v3bsXW7duRbNmzZCVlYXJkyfjmWeeQceOHdG+fXs88cQTyMjIwE033WRd0BIL9UyF09rXqIizJdp/ogL3vLM5hPlI23FJDsI+Xu0if97X/7PtMP6z7XBo84p0Wk3ofJYmMps2bcIvfvEL5XXd2JZx48bhrbfewiOPPILy8nL85je/QUlJCQYMGIAlS5YgNjbWqpAdyamnyS5v1ww39MjA4ZLTmucRFxOJX/duo2NURKS32y7Pwv4TFThdXRvSfGKiInBHv7Y6RUVmsTSRufrqq/0OeHS5XJgxYwZmzJhhYlT2w/PoDYuNjsQrt/e0OgxdODPVJNJHtzbJ+PuEvlaHQRZhnzkpmBARyYWDu4kCYyJDvEeJRbQsdiabzsXL8Ym0YSKjEescIjIK6xei4DGRCZFMj3V36qBdp5FniyGrSVR9ENkWExly7kMjiWyOiQ5RYExkHIG1ndNx/ITzcQ0TacNEhoiIiGyLiQwpR/vsxpYf15GzNHY6l+PdiILHRIbIIjxbRPVxeyDShomMg7AiJCKicMNERiMn5gw8bWEOmS7ZJ2txUyAKHROZEMlQD7EyJCKicMVEhohIBvW6eXmamCh4TGRIwRviyY/riIjIFxMZB9F6FMejPyJrBEpLOZ6KKDAmMkQWYf5IRBQ6JjIOoNcxGw/+iKxT/yZ47CUlCh4TGY2c9Owb3kXUXMwXiYj0w0SGyAYclDdTPYHGwDDpJQqMiUyoWNMQERFZJsrqAEg/a3f9hP0nylV/b9uBUgOiISNwHJNzsdeNSBsmMg4QFXG2dXv2k+90mQ8RWYs5DVHwmMg4wP3XdMQHmw+GdEQXFenC+P7t9QuKiALioQNR6JjIOMBtV2ThtiuyrA6DVHLSlW9kDJ5KJAqMg32JbICXyDsf1zCRNkxkNGKlQ1rxKJuISD9MZELENomItGosqeVpR6LgMZEhIiIi22IiQ0QkgYZ6YdjjSxQYExkiIiKyLV5+TWSR9zbsxxc7fwpq2qOeSoOjISs09qwljpAhCh4TGSKTtUx0AwD2HCvHnmPqHinR4ufvEhHRWUxkiEw25dpOuCyzKarO1Kr6XlRkBAZ1amFQVGS1hnphAj0dm4iYyGjGqyNJq/iYKAzv3srqMIiIHIGDfUPEIyYiIiLrMJEhIpINe3yJgsZEhohIAjxdTaSNLRKZWbNmoV27doiNjUXfvn2xYcMGq0MiIjIcT1wTBSZ9IvP+++9j6tSpmD59OjZv3owePXpg6NChOHr0qNWhERGFjMPsiEIjfSLz5z//GXfffTfGjx+PSy65BHPmzEF8fDzefPNNS+Mqqai29PeJyFmOlJ7GwZMVOHiyAidYvxAFTerLr6urq1FQUIBp06Yp70VERCA3Nxf5+fkNfqeqqgpVVVXKa4/HY0hsLy/fach8iSg83fDXtVaHQGRLUvfI/PTTT6itrUVaWprP+2lpaSgqKmrwO3l5eUhOTlb+MjMzDYmtQ/MmcEdF4IYeGYbMn4jCw409MuCOirjgL9EdhaFd060Oj0h6LtHQI1clcfjwYbRu3RpfffUVcnJylPcfeeQRrF69GuvXr7/gOw31yGRmZqK0tBRJSUmmxE1ERESh8Xg8SE5ODth+S31qqXnz5oiMjERxcbHP+8XFxUhPb/hIxe12w+3m82iIiIjCgdSnlmJiYtC7d28sX75cec/r9WL58uU+PTREREQUnqTukQGAqVOnYty4cejTpw+uuOIKvPTSSygvL8f48eOtDo2IiIgsJn0ic+utt+LYsWN48sknUVRUhMsuuwxLliy5YAAwERERhR+pB/vqIdjBQkRERCSPYNtvqcfIEBEREfnDRIaIiIhsi4kMERER2RYTGSIiIrItJjJERERkW0xkiIiIyLaYyBAREZFtMZEhIiIi22IiQ0RERLYl/SMKQlV342KPx2NxJERERBSsunY70AMIHJ/IlJWVAQAyMzMtjoSIiIjUKisrQ3JycqOfO/5ZS16vF4cPH0ZiYiJcLpdu8/V4PMjMzMSBAwfC7hlOLHv4lT1cyw2w7OFY9nAtNyBX2YUQKCsrQ0ZGBiIiGh8J4/gemYiICLRp08aw+SclJVm+sq3Csodf2cO13ADLHo5lD9dyA/KU3V9PTB0O9iUiIiLbYiJDREREtsVERiO3243p06fD7XZbHYrpWPbwK3u4lhtg2cOx7OFabsCeZXf8YF8iIiJyLvbIEBERkW0xkSEiIiLbYiJDREREtsVEhoiIiGyLiYxGs2bNQrt27RAbG4u+fftiw4YNVoekSl5eHi6//HIkJiaiZcuWuOmmm1BYWOgzTWVlJSZOnIjU1FQkJCRg1KhRKC4u9plm//79GD58OOLj49GyZUs8/PDDOHPmjM80q1atQq9eveB2u3HxxRfjrbfeMrp4QZs5cyZcLhcmT56svOfkch86dAhjxoxBamoq4uLi0K1bN2zatEn5XAiBJ598Eq1atUJcXBxyc3Oxc+dOn3mcOHECo0ePRlJSElJSUjBhwgScOnXKZ5qvv/4aAwcORGxsLDIzM/HCCy+YUr6G1NbW4oknnkD79u0RFxeHiy66CE8//bTP81ucUu4vvvgCI0aMQEZGBlwuFxYvXuzzuZnlXLhwIbKzsxEbG4tu3brhk08+0b289fkre01NDR599FF069YNTZo0QUZGBu644w4cPnzYZx52LHugdV7fPffcA5fLhZdeesnnfTuW24cg1RYsWCBiYmLEm2++Kb799ltx9913i5SUFFFcXGx1aEEbOnSomDdvnti+fbvYunWr+OUvfymysrLEqVOnlGnuuecekZmZKZYvXy42bdok+vXrJ6688krl8zNnzohLL71U5Obmii1btohPPvlENG/eXEybNk2ZZs+ePSI+Pl5MnTpV7NixQ/zlL38RkZGRYsmSJaaWtyEbNmwQ7dq1E927dxcPPPCA8r5Ty33ixAnRtm1bceedd4r169eLPXv2iKVLl4pdu3Yp08ycOVMkJyeLxYsXi23btokbbrhBtG/fXpw+fVqZ5rrrrhM9evQQ69atE19++aW4+OKLxe233658XlpaKtLS0sTo0aPF9u3bxT/+8Q8RFxcnXnvtNVPLW+fZZ58Vqamp4qOPPhJ79+4VCxcuFAkJCeLll19WpnFKuT/55BPx+OOPi0WLFgkA4sMPP/T53Kxyrl27VkRGRooXXnhB7NixQ/z+978X0dHR4ptvvrGk7CUlJSI3N1e8//774vvvvxf5+fniiiuuEL179/aZhx3LHmid11m0aJHo0aOHyMjIEC+++KLPZ3Ysd31MZDS44oorxMSJE5XXtbW1IiMjQ+Tl5VkYVWiOHj0qAIjVq1cLIc7u+NHR0WLhwoXKNN99950AIPLz84UQZ3egiIgIUVRUpEwze/ZskZSUJKqqqoQQQjzyyCOia9euPr916623iqFDhxpdJL/KyspEx44dxbJly8SgQYOURMbJ5X700UfFgAEDGv3c6/WK9PR08cc//lF5r6SkRLjdbvGPf/xDCCHEjh07BACxceNGZZpPP/1UuFwucejQISGEEK+++qpo2rSpsizqfrtz5856Fykow4cPF3fddZfPeyNHjhSjR48WQji33Oc3amaW85ZbbhHDhw/3iadv377it7/9ra5lbIy/Br3Ohg0bBACxb98+IYQzyt5YuQ8ePChat24ttm/fLtq2beuTyDih3Dy1pFJ1dTUKCgqQm5urvBcREYHc3Fzk5+dbGFloSktLAQDNmjUDABQUFKCmpsannNnZ2cjKylLKmZ+fj27duiEtLU2ZZujQofB4PPj222+VaerPo24aq5fVxIkTMXz48Atic3K5//Of/6BPnz64+eab0bJlS/Ts2ROvv/668vnevXtRVFTkE3dycjL69u3rU/aUlBT06dNHmSY3NxcRERFYv369Ms1VV12FmJgYZZqhQ4eisLAQJ0+eNLqYF7jyyiuxfPly/PDDDwCAbdu2Yc2aNRg2bBgA55b7fGaWU8bt/3ylpaVwuVxISUkB4Nyye71ejB07Fg8//DC6du16wedOKDcTGZV++ukn1NbW+jRiAJCWloaioiKLogqN1+vF5MmT0b9/f1x66aUAgKKiIsTExCg7eZ365SwqKmpwOdR95m8aj8eD06dPG1GcgBYsWIDNmzcjLy/vgs+cXO49e/Zg9uzZ6NixI5YuXYp7770X999/P+bPnw/gXOz+tu2ioiK0bNnS5/OoqCg0a9ZM1fIx02OPPYbbbrsN2dnZiI6ORs+ePTF58mSMHj3aJyanlft8ZpazsWlkWA7A2XFwjz76KG6//XblwYhOLfvzzz+PqKgo3H///Q1+7oRyO/7p1xTYxIkTsX37dqxZs8bqUAx34MABPPDAA1i2bBliY2OtDsdUXq8Xffr0wXPPPQcA6NmzJ7Zv3445c+Zg3LhxFkdnnH/+859499138d5776Fr167YunUrJk+ejIyMDEeXmxpWU1ODW265BUIIzJ492+pwDFVQUICXX34Zmzdvhsvlsjocw7BHRqXmzZsjMjLygqtYiouLkZ6eblFU2t1333346KOPsHLlSrRp00Z5Pz09HdXV1SgpKfGZvn4509PTG1wOdZ/5myYpKQlxcXF6FyeggoICHD16FL169UJUVBSioqKwevVqvPLKK4iKikJaWpojyw0ArVq1wiWXXOLzXpcuXbB//34A52L3t22np6fj6NGjPp+fOXMGJ06cULV8zPTwww8rvTLdunXD2LFjMWXKFKVHzqnlPp+Z5WxsGquXQ10Ss2/fPixbtkzpjQGcWfYvv/wSR48eRVZWllLf7du3Dw8++CDatWunxGv3cjORUSkmJga9e/fG8uXLlfe8Xi+WL1+OnJwcCyNTRwiB++67Dx9++CFWrFiB9u3b+3zeu3dvREdH+5SzsLAQ+/fvV8qZk5ODb775xmcnqKsc6hrMnJwcn3nUTWPVsrrmmmvwzTffYOvWrcpfnz59MHr0aOX/Tiw3APTv3/+CS+x/+OEHtG3bFgDQvn17pKen+8Tt8Xiwfv16n7KXlJSgoKBAmWbFihXwer3o27evMs0XX3yBmpoaZZply5ahc+fOaNq0qWHla0xFRQUiInyrusjISHi9XgDOLff5zCynjNt/XRKzc+dOfP7550hNTfX53IllHzt2LL7++muf+i4jIwMPP/wwli5dqsRr+3IbPpzYgRYsWCDcbrd46623xI4dO8RvfvMbkZKS4nMVi+zuvfdekZycLFatWiWOHDmi/FVUVCjT3HPPPSIrK0usWLFCbNq0SeTk5IicnBzl87rLkIcMGSK2bt0qlixZIlq0aNHgZcgPP/yw+O6778SsWbMsvwz5fPWvWhLCueXesGGDiIqKEs8++6zYuXOnePfdd0V8fLx45513lGlmzpwpUlJSxL///W/x9ddfixtvvLHBy3N79uwp1q9fL9asWSM6duzoc6lmSUmJSEtLE2PHjhXbt28XCxYsEPHx8ZZdfj1u3DjRunVr5fLrRYsWiebNm4tHHnlEmcYp5S4rKxNbtmwRW7ZsEQDEn//8Z7Flyxblyhyzyrl27VoRFRUl/u///k989913Yvr06YZfiuuv7NXV1eKGG24Qbdq0EVu3bvWp8+pfiWPHsgda5+c7/6olu5a7PiYyGv3lL38RWVlZIiYmRlxxxRVi3bp1VoekCoAG/+bNm6dMc/r0afG73/1ONG3aVMTHx4tf/epX4siRIz7z+fHHH8WwYcNEXFycaN68uXjwwQdFTU2NzzQrV64Ul112mYiJiREdOnTw+Q0ZnJ/IOLnc//3vf8Wll14q3G63yM7OFn/72998Pvd6veKJJ54QaWlpwu12i2uuuUYUFhb6THP8+HFx++23i4SEBJGUlCTGjx8vysrKfKbZtm2bGDBggHC73aJ169Zi5syZhpetMR6PRzzwwAMiKytLxMbGig4dOojHH3/cpwFzSrlXrlzZ4H49btw4IYS55fznP/8pOnXqJGJiYkTXrl3Fxx9/bFi5hfBf9r179zZa561cudLWZQ+0zs/XUCJjx3LX5xKi3u0tiYiIiGyEY2SIiIjItpjIEBERkW0xkSEiIiLbYiJDREREtsVEhoiIiGyLiQwRERHZFhMZIiIisi0mMkRERGRbTGSISHd33nknbrrpJst+f+zYscpTvo322GOPYdKkSab8FhFdiHf2JSJVXC6X38+nT5+OKVOmQAiBlJQUc4KqZ9u2bRg8eDD27duHhIQEw3/vp59+QocOHbB161Z06NDB8N8jIl9MZIhIlaKiIuX/77//Pp588kmfp2onJCSYkkA05n/+538QFRWFOXPmmPabN998M9q1a4c//vGPpv0mEZ3FU0tEpEp6erryl5ycDJfL5fNeQkLCBaeWrr76akyaNAmTJ09G06ZNkZaWhtdffx3l5eUYP348EhMTcfHFF+PTTz/1+a3t27dj2LBhSEhIQFpaGsaOHYuffvqp0dhqa2vxr3/9CyNGjPB5/9VXX0XHjh0RGxuLtLQ0/PrXv1Y+83q9yMvLQ/v27REXF4cePXrgX//6l8/3v/32W1x//fVISkpCYmIiBg4ciN27dyufjxgxAgsWLNCyOIkoRExkiMgU8+fPR/PmzbFhwwZMmjQJ9957L26++WZceeWV2Lx5M4YMGYKxY8eioqICAFBSUoLBgwejZ8+e2LRpE5YsWYLi4mLccsstjf7G119/jdLSUvTp00d5b9OmTbj//vsxY8YMFBYWYsmSJbjqqquUz/Py8vD2229jzpw5+PbbbzFlyhSMGTMGq1evBgAcOnQIV111FdxuN1asWIGCggLcddddOHPmjDKPK664AgcPHsSPP/6o81IjooBMecY2ETnSvHnzRHJy8gXvjxs3Ttx4443K60GDBokBAwYor8+cOSOaNGkixo4dq7x35MgRAUDk5+cLIYR4+umnxZAhQ3zme+DAAQFAFBYWNhjPhx9+KCIjI4XX61Xe++CDD0RSUpLweDwXTF9ZWSni4+PFV1995fP+hAkTxO233y6EEGLatGmiffv2orq6upGlIERpaakAIFatWtXoNERkjCiL8ygiChPdu3dX/h8ZGYnU1FR069ZNeS8tLQ0AcPToUQBnB+2uXLmywfE2u3fvRqdOnS54//Tp03C73T4Dkq+99lq0bdsWHTp0wHXXXYfrrrsOv/rVrxAfH49du3ahoqIC1157rc98qqur0bNnTwDA1q1bMXDgQERHRzdatri4OABQepOIyDxMZIjIFOcnAi6Xy+e9uuTD6/UCAE6dOoURI0bg+eefv2BerVq1avA3mjdvjoqKClRXVyMmJgYAkJiYiM2bN2PVqlX47LPP8OSTT+Kpp57Cxo0bcerUKQDAxx9/jNatW/vMy+12AziXpPhz4sQJAECLFi0CTktE+mIiQ0RS6tWrFz744AO0a9cOUVHBVVWXXXYZAGDHjh3K/wEgKioKubm5yM3NxfTp05GSkoIVK1bg2muvhdvtxv79+zFo0KAG59m9e3fMnz8fNTU1jfbKbN++HdHR0ejatauqMhJR6DjYl4ikNHHiRJw4cQK33347Nm7ciN27d2Pp0qUYP348amtrG/xOixYt0KtXL6xZs0Z576OPPsIrr7yCrVu3Yt++fXj77bfh9XrRuXNnJCYm4qGHHsKUKVMwf/587N69G5s3b8Zf/vIXzJ8/HwBw3333wePx4LbbbsOmTZuwc+dO/P3vf/e55PzLL7/EwIEDg+q9ISJ9MZEhIillZGRg7dq1qK2txZAhQ9CtWzdMnjwZKSkpiIhovOr6n//5H7z77rvK65SUFCxatAiDBw9Gly5dMGfOHPzjH/9Qek+efvppPPHEE8jLy0OXLl1w3XXX4eOPP0b79u0BAKmpqVixYgVOnTqFQYMGoXfv3nj99dd9emcWLFiAu+++26AlQUT+8IZ4ROQop0+fRufOnfH+++8jJyfH8N/79NNP8eCDD+Lrr78O+hQYEemHPTJE5ChxcXF4++23/d44T0/l5eWYN28ekxgii7BHhoiIiGyLPTJERERkW0xkiIiIyLaYyBAREZFtMZEhIiIi22IiQ0RERLbFRIaIiIhsi4kMERER2RYTGSIiIrItJjJERERkW/8fq1XLkisRNokAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# decide model input-outputs and scale data\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "X = data[['T1','Q1']].values\n",
        "y = data[['T1']].values\n",
        "\n",
        "X_scaler = StandardScaler()\n",
        "X_scaled = X_scaler.fit_transform(X)\n",
        "\n",
        "y_scaler = StandardScaler()\n",
        "y_scaled = y_scaler.fit_transform(y)"
      ],
      "metadata": {
        "id": "lHYHb-vp0KY-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# rearrange X data into (# sequence samples, # time steps, # features) form\n",
        "nTimeSteps = 70\n",
        "X_train_sequence = []\n",
        "y_train_sequence = []\n",
        "\n",
        "for sample in range(nTimeSteps, X_scaled.shape[0]):\n",
        "    X_train_sequence.append(X_scaled[sample-nTimeSteps:sample,:])\n",
        "    y_train_sequence.append(y_scaled[sample])\n",
        "\n",
        "# X conversion: convert list of (time steps, features) arrays into (samples, time steps, features) array\n",
        "X_train_sequence, y_train_sequence = np.array(X_train_sequence), np.array(y_train_sequence)"
      ],
      "metadata": {
        "id": "DNSZBPqW0Ncd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#%% import Keras libraries\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from tensorflow.keras import regularizers"
      ],
      "metadata": {
        "id": "YEahkxnd0P4I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define model\n",
        "model = Sequential()\n",
        "model.add(LSTM(units=25, kernel_regularizer=regularizers.L1(0.001), input_shape=(nTimeSteps,2)))\n",
        "model.add(Dense(units=1))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "xs0P0Izf0Ttz",
        "outputId": "444926d4-26a6-498f-e3dc-746d7f1a1d5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m)             │         \u001b[38;5;34m2,800\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m26\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,800</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,826\u001b[0m (11.04 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,826</span> (11.04 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,826\u001b[0m (11.04 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,826</span> (11.04 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compile model\n",
        "model.compile(loss='mse', optimizer='Adam')"
      ],
      "metadata": {
        "id": "QV7v5w3U0XdT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fit model with early stopping\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "es = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
        "history = model.fit(X_train_sequence, y_train_sequence, epochs=100, batch_size=250, validation_split=0.3, callbacks=[es])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GpIP2US0aO0",
        "outputId": "9d100303-8f42-4816-ec2a-36b9acd9540d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 76ms/step - loss: 0.6057 - val_loss: 0.2119\n",
            "Epoch 2/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 54ms/step - loss: 0.0601 - val_loss: 0.0900\n",
            "Epoch 3/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 53ms/step - loss: 0.0311 - val_loss: 0.0740\n",
            "Epoch 4/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 53ms/step - loss: 0.0259 - val_loss: 0.0662\n",
            "Epoch 5/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 52ms/step - loss: 0.0221 - val_loss: 0.0600\n",
            "Epoch 6/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 67ms/step - loss: 0.0190 - val_loss: 0.0559\n",
            "Epoch 7/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 48ms/step - loss: 0.0165 - val_loss: 0.0511\n",
            "Epoch 8/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 0.0145 - val_loss: 0.0484\n",
            "Epoch 9/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 45ms/step - loss: 0.0128 - val_loss: 0.0453\n",
            "Epoch 10/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 58ms/step - loss: 0.0115 - val_loss: 0.0425\n",
            "Epoch 11/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 54ms/step - loss: 0.0102 - val_loss: 0.0408\n",
            "Epoch 12/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0092 - val_loss: 0.0382\n",
            "Epoch 13/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0084 - val_loss: 0.0361\n",
            "Epoch 14/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 53ms/step - loss: 0.0078 - val_loss: 0.0346\n",
            "Epoch 15/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 44ms/step - loss: 0.0073 - val_loss: 0.0333\n",
            "Epoch 16/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 73ms/step - loss: 0.0068 - val_loss: 0.0323\n",
            "Epoch 17/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0065 - val_loss: 0.0310\n",
            "Epoch 18/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 45ms/step - loss: 0.0062 - val_loss: 0.0298\n",
            "Epoch 19/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0059 - val_loss: 0.0289\n",
            "Epoch 20/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 53ms/step - loss: 0.0057 - val_loss: 0.0276\n",
            "Epoch 21/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 65ms/step - loss: 0.0054 - val_loss: 0.0268\n",
            "Epoch 22/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 50ms/step - loss: 0.0052 - val_loss: 0.0262\n",
            "Epoch 23/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0050 - val_loss: 0.0248\n",
            "Epoch 24/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 45ms/step - loss: 0.0048 - val_loss: 0.0238\n",
            "Epoch 25/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 45ms/step - loss: 0.0047 - val_loss: 0.0224\n",
            "Epoch 26/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 63ms/step - loss: 0.0046 - val_loss: 0.0214\n",
            "Epoch 27/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 52ms/step - loss: 0.0044 - val_loss: 0.0194\n",
            "Epoch 28/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0043 - val_loss: 0.0196\n",
            "Epoch 29/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 53ms/step - loss: 0.0042 - val_loss: 0.0176\n",
            "Epoch 30/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0040 - val_loss: 0.0176\n",
            "Epoch 31/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 47ms/step - loss: 0.0039 - val_loss: 0.0161\n",
            "Epoch 32/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 67ms/step - loss: 0.0038 - val_loss: 0.0160\n",
            "Epoch 33/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 45ms/step - loss: 0.0037 - val_loss: 0.0148\n",
            "Epoch 34/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0036 - val_loss: 0.0143\n",
            "Epoch 35/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 45ms/step - loss: 0.0035 - val_loss: 0.0146\n",
            "Epoch 36/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 71ms/step - loss: 0.0034 - val_loss: 0.0142\n",
            "Epoch 37/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 46ms/step - loss: 0.0033 - val_loss: 0.0128\n",
            "Epoch 38/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0032 - val_loss: 0.0133\n",
            "Epoch 39/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 53ms/step - loss: 0.0032 - val_loss: 0.0119\n",
            "Epoch 40/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 53ms/step - loss: 0.0031 - val_loss: 0.0116\n",
            "Epoch 41/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 64ms/step - loss: 0.0030 - val_loss: 0.0127\n",
            "Epoch 42/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 53ms/step - loss: 0.0030 - val_loss: 0.0118\n",
            "Epoch 43/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0029 - val_loss: 0.0112\n",
            "Epoch 44/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 52ms/step - loss: 0.0028 - val_loss: 0.0107\n",
            "Epoch 45/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 64ms/step - loss: 0.0027 - val_loss: 0.0104\n",
            "Epoch 46/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 50ms/step - loss: 0.0027 - val_loss: 0.0110\n",
            "Epoch 47/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0026 - val_loss: 0.0092\n",
            "Epoch 48/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0025 - val_loss: 0.0089\n",
            "Epoch 49/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 46ms/step - loss: 0.0025 - val_loss: 0.0098\n",
            "Epoch 50/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 0.0024 - val_loss: 0.0084\n",
            "Epoch 51/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 60ms/step - loss: 0.0023 - val_loss: 0.0086\n",
            "Epoch 52/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 53ms/step - loss: 0.0023 - val_loss: 0.0082\n",
            "Epoch 53/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0022 - val_loss: 0.0079\n",
            "Epoch 54/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0022 - val_loss: 0.0082\n",
            "Epoch 55/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 0.0021 - val_loss: 0.0076\n",
            "Epoch 56/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 63ms/step - loss: 0.0020 - val_loss: 0.0076\n",
            "Epoch 57/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 52ms/step - loss: 0.0020 - val_loss: 0.0075\n",
            "Epoch 58/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0019 - val_loss: 0.0067\n",
            "Epoch 59/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0018 - val_loss: 0.0077\n",
            "Epoch 60/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0018 - val_loss: 0.0081\n",
            "Epoch 61/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 45ms/step - loss: 0.0018 - val_loss: 0.0060\n",
            "Epoch 62/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 69ms/step - loss: 0.0017 - val_loss: 0.0070\n",
            "Epoch 63/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0016 - val_loss: 0.0065\n",
            "Epoch 64/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 53ms/step - loss: 0.0016 - val_loss: 0.0056\n",
            "Epoch 65/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 0.0015 - val_loss: 0.0057\n",
            "Epoch 66/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 46ms/step - loss: 0.0015 - val_loss: 0.0056\n",
            "Epoch 67/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 54ms/step - loss: 0.0014 - val_loss: 0.0061\n",
            "Epoch 68/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 60ms/step - loss: 0.0014 - val_loss: 0.0057\n",
            "Epoch 69/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0013 - val_loss: 0.0045\n",
            "Epoch 70/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0013 - val_loss: 0.0055\n",
            "Epoch 71/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 45ms/step - loss: 0.0013 - val_loss: 0.0049\n",
            "Epoch 72/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 45ms/step - loss: 0.0012 - val_loss: 0.0044\n",
            "Epoch 73/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 62ms/step - loss: 0.0012 - val_loss: 0.0043\n",
            "Epoch 74/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0011 - val_loss: 0.0038\n",
            "Epoch 75/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0011 - val_loss: 0.0039\n",
            "Epoch 76/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0011 - val_loss: 0.0035\n",
            "Epoch 77/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 0.0011 - val_loss: 0.0033\n",
            "Epoch 78/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 0.0010 - val_loss: 0.0038\n",
            "Epoch 79/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 60ms/step - loss: 9.5481e-04 - val_loss: 0.0033\n",
            "Epoch 80/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 53ms/step - loss: 9.0434e-04 - val_loss: 0.0023\n",
            "Epoch 81/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 9.0797e-04 - val_loss: 0.0027\n",
            "Epoch 82/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 46ms/step - loss: 8.5825e-04 - val_loss: 0.0029\n",
            "Epoch 83/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 8.3176e-04 - val_loss: 0.0021\n",
            "Epoch 84/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 63ms/step - loss: 8.6294e-04 - val_loss: 0.0028\n",
            "Epoch 85/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 51ms/step - loss: 7.6039e-04 - val_loss: 0.0022\n",
            "Epoch 86/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 8.5399e-04 - val_loss: 0.0025\n",
            "Epoch 87/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 45ms/step - loss: 7.3082e-04 - val_loss: 0.0023\n",
            "Epoch 88/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 7.5960e-04 - val_loss: 0.0023\n",
            "Epoch 89/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 45ms/step - loss: 7.3726e-04 - val_loss: 0.0019\n",
            "Epoch 90/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 65ms/step - loss: 6.9114e-04 - val_loss: 0.0030\n",
            "Epoch 91/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 46ms/step - loss: 7.2568e-04 - val_loss: 0.0019\n",
            "Epoch 92/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 48ms/step - loss: 6.8129e-04 - val_loss: 0.0019\n",
            "Epoch 93/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 53ms/step - loss: 6.9253e-04 - val_loss: 0.0022\n",
            "Epoch 94/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 69ms/step - loss: 8.2000e-04 - val_loss: 0.0020\n",
            "Epoch 95/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 47ms/step - loss: 6.2411e-04 - val_loss: 0.0032\n",
            "Epoch 96/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 6.9280e-04 - val_loss: 0.0019\n",
            "Epoch 97/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 46ms/step - loss: 6.2142e-04 - val_loss: 0.0021\n",
            "Epoch 98/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 72ms/step - loss: 5.9096e-04 - val_loss: 0.0019\n",
            "Epoch 99/100\n",
            "\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 48ms/step - loss: 6.3235e-04 - val_loss: 0.0019\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot validation curve\n",
        "plt.figure()\n",
        "plt.title('Validation Curves')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('MSE')\n",
        "plt.plot(history.history['loss'], label='train')\n",
        "plt.plot(history.history['val_loss'], label='val')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "G4PMZQn42LbU",
        "outputId": "fad0aeae-cc4f-45e4-c73a-22b086782b6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check fit on training data\n",
        "y_train_sequence_pred = model.predict(X_train_sequence)\n",
        "y_measured = y_scaler.inverse_transform(y_train_sequence)\n",
        "y_pred =  y_scaler.inverse_transform(y_train_sequence_pred)\n",
        "\n",
        "# plot\n",
        "plt.figure()\n",
        "plt.plot(y_pred, 'r-', label='LSTM prediction')\n",
        "plt.plot(y_measured, 'k--', label='raw measurements')\n",
        "plt.ylabel('Temperature (°C)')\n",
        "plt.xlabel('Time (sec)')\n",
        "plt.legend()\n",
        "plt.title('Training data')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "13VrlIq42Oo4",
        "outputId": "c363c170-28a1-4b5c-ffd8-54ed279e456d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m448/448\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 13ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Training data')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# read test data\n",
        "data_test = pd.read_csv('TCLab_test_data.txt')\n",
        "X_test = data_test[['T1','Q1']].values\n",
        "y_test = data_test[['T1']].values\n",
        "\n",
        "# scale data\n",
        "X_test_scaled = X_scaler.transform(X_test)\n",
        "y_test_scaled = y_scaler.transform(y_test)\n",
        "\n",
        "# re-arrange data into sequence form\n",
        "X_test_sequence = []\n",
        "y_test_sequence = []\n",
        "\n",
        "for sample in range(nTimeSteps, X_test_scaled.shape[0]):\n",
        "    X_test_sequence.append(X_test_scaled[sample-nTimeSteps:sample,:])\n",
        "    y_test_sequence.append(y_test_scaled[sample])\n",
        "\n",
        "X_test_sequence, y_test_sequence = np.array(X_test_sequence), np.array(y_test_sequence)"
      ],
      "metadata": {
        "id": "5CVj5b7A2bQU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# predict y_test\n",
        "y_test_sequence_pred = model.predict(X_test_sequence)\n",
        "y_test_pred =  y_scaler.inverse_transform(y_test_sequence_pred)\n",
        "\n",
        "# plot\n",
        "nRows_test = y_test.shape[0]\n",
        "\n",
        "# temperature\n",
        "plt.figure()\n",
        "plt.plot(np.arange(nTimeSteps, nRows_test), y_test_pred, 'r-', label='LSTM prediction')\n",
        "plt.plot(np.arange(nRows_test), y_test, 'k--', label='raw measurements')\n",
        "plt.ylabel('Temperature (°C)')\n",
        "plt.xlabel('Time (sec)')\n",
        "plt.legend()\n",
        "plt.title('Test data')\n",
        "\n",
        "# heater power\n",
        "plt.figure()\n",
        "plt.plot(X_test[:,1])\n",
        "plt.ylabel('Heater Power')\n",
        "plt.xlabel('Time (sec)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 940
        },
        "id": "Qpa5ayu32hDT",
        "outputId": "10b8a1ee-aea4-402c-d58a-de617529a694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Time (sec)')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. Classificação de Falhas via LSTM para o Processo Tennessee Eastman"
      ],
      "metadata": {
        "id": "FhLWfcNU2lMP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyreadr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T3g88jyY6Uit",
        "outputId": "790b7a03-66f0-48fd-94b1-a6c9e3ac096d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyreadr\n",
            "  Downloading pyreadr-0.5.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.4 kB)\n",
            "Requirement already satisfied: pandas>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from pyreadr) (2.2.2)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2.0->pyreadr) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.2.0->pyreadr) (2025.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=1.2.0->pyreadr) (1.17.0)\n",
            "Downloading pyreadr-0.5.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (411 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m411.7/411.7 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pyreadr\n",
            "Successfully installed pyreadr-0.5.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import required packages\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "BIy5-R-g2s45"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# read data\n",
        "import pyreadr\n",
        "fault_free_training_data = pyreadr.read_r('TEP_FaultFree_Training.RData')['fault_free_training'] # pandas dataframe\n",
        "fault_free_testing_data = pyreadr.read_r('TEP_FaultFree_Testing.RData')['fault_free_testing']\n",
        "faulty_training_data = pyreadr.read_r('TEP_Faulty_Training.RData')['faulty_training']\n",
        "faulty_testing_data = pyreadr.read_r('TEP_Faulty_Testing.RData')['faulty_testing']\n",
        "\n",
        "fault_free_training_data.head()"
      ],
      "metadata": {
        "id": "dAG6zGJh8KrR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# remove fault 3,9,15 data from faulty dataset\n",
        "faulty_training_data = faulty_training_data[faulty_training_data['faultNumber'] != 3]\n",
        "faulty_training_data = faulty_training_data[faulty_training_data['faultNumber'] != 9]\n",
        "faulty_training_data = faulty_training_data[faulty_training_data['faultNumber'] != 15]\n",
        "\n",
        "faulty_testing_data = faulty_testing_data[faulty_testing_data['faultNumber'] != 3]\n",
        "faulty_testing_data = faulty_testing_data[faulty_testing_data['faultNumber'] != 9]\n",
        "faulty_testing_data = faulty_testing_data[faulty_testing_data['faultNumber'] != 15]"
      ],
      "metadata": {
        "id": "VfvVRWyG8N8-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# separate validation dataset out of training dataset and create imbalanced faulty dataset\n",
        "fault_free_validation_data = fault_free_training_data[fault_free_training_data['simulationRun'] > 400]\n",
        "fault_free_training_data = fault_free_training_data[fault_free_training_data['simulationRun'] <= 400]\n",
        "faulty_validation_data = faulty_training_data[faulty_training_data['simulationRun'] > 490]\n",
        "faulty_training_data = faulty_training_data[faulty_training_data['simulationRun'] <= 50]\n",
        "\n",
        "# convert to numpy\n",
        "fault_free_training_data = fault_free_training_data.values\n",
        "fault_free_validation_data = fault_free_validation_data.values\n",
        "fault_free_testing_data = fault_free_testing_data.values\n",
        "faulty_training_data = faulty_training_data.values\n",
        "faulty_validation_data = faulty_validation_data.values\n",
        "faulty_testing_data = faulty_testing_data.values"
      ],
      "metadata": {
        "id": "4nfXBD-E8R2W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# complete training, validation, and test datasets\n",
        "training_data = np.vstack((fault_free_training_data,faulty_training_data))\n",
        "validation_data = np.vstack((fault_free_validation_data,faulty_validation_data))\n",
        "testing_data = np.vstack((fault_free_testing_data,faulty_testing_data))\n",
        "\n",
        "# separate X and y data\n",
        "X_train = training_data[:,3:]\n",
        "X_val = validation_data[:,3:]\n",
        "X_test = testing_data[:,3:]\n",
        "\n",
        "y_train = training_data[:,0]\n",
        "y_val = validation_data[:,0]\n",
        "y_test = testing_data[:,0]\n",
        "\n",
        "# scale data\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "X_scaler = StandardScaler()\n",
        "X_train_scaled = X_scaler.fit_transform(X_train)\n",
        "X_val_scaled = X_scaler.transform(X_val)\n",
        "X_test_scaled = X_scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "py4DjcaX8VIB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# rearrange X data into (# sequence samples, # time steps, # features) form\n",
        "nTimeStepsTrain = 500 # length of a simulation run in training data\n",
        "nTimeStepsTest = 960 # length of a simulation run in testing data\n",
        "X_train_sequence = []\n",
        "y_train_sequence = []\n",
        "X_val_sequence = []\n",
        "y_val_sequence = []\n",
        "X_test_sequence = []\n",
        "y_test_sequence = []\n",
        "\n",
        "for sample in range(0, X_train_scaled.shape[0], nTimeStepsTrain):\n",
        "    X_train_sequence.append(X_train_scaled[sample:sample+nTimeStepsTrain,:])\n",
        "    y_train_sequence.append(y_train[sample])\n",
        "\n",
        "for sample in range(0, X_val_scaled.shape[0], nTimeStepsTrain):\n",
        "    X_val_sequence.append(X_val_scaled[sample:sample+nTimeStepsTrain,:])\n",
        "    y_val_sequence.append(y_val[sample])\n",
        "\n",
        "for sample in range(0, X_test_scaled.shape[0], nTimeStepsTest):\n",
        "    X_test_sequence.append(X_test_scaled[sample:sample+nTimeStepsTest,:])\n",
        "    y_test_sequence.append(y_test[sample])\n",
        "\n",
        "X_train_sequence, y_train_sequence = np.array(X_train_sequence), np.array(y_train_sequence)\n",
        "X_val_sequence, y_val_sequence = np.array(X_val_sequence), np.array(y_val_sequence)\n",
        "X_test_sequence, y_test_sequence = np.array(X_test_sequence), np.array(y_test_sequence)"
      ],
      "metadata": {
        "id": "d2Pb3pgN8a0j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# convert fault class labels to one-hot encoded form\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "Y_train_sequence = to_categorical(y_train_sequence, num_classes=21)\n",
        "Y_val_sequence = to_categorical(y_val_sequence, num_classes=21)\n",
        "Y_test_sequence = to_categorical(y_test_sequence, num_classes=21)"
      ],
      "metadata": {
        "id": "CPsfMhMa8dXf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import packages\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from tensorflow.keras import regularizers\n",
        "\n",
        "# define model\n",
        "model = Sequential()\n",
        "model.add(LSTM(units=128, kernel_regularizer=regularizers.L1(0.0001), return_sequences=True, input_shape=(nTimeStepsTrain,52)))\n",
        "model.add(LSTM(units=64, kernel_regularizer=regularizers.L1(0.0001)))\n",
        "model.add(Dense(21, activation='softmax'))\n",
        "\n",
        "# model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "ERukXJKM8f0m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compile model\n",
        "model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\n",
        "\n",
        "# fit model with early stopping\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "es = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)\n",
        "history = model.fit(X_train_sequence, Y_train_sequence, epochs=100, batch_size=250, validation_data=(X_val_sequence,Y_val_sequence), callbacks=[es])"
      ],
      "metadata": {
        "id": "h3O7J3Ft8kpT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plot validation curve\n",
        "plt.figure()\n",
        "plt.title('Validation Curves: Loss')\n",
        "plt.xlabel('Epoch'), plt.ylabel('Loss')\n",
        "plt.plot(history.history['loss'], label='training')\n",
        "plt.plot(history.history['val_loss'], label='validation')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()\n",
        "\n",
        "plt.figure()\n",
        "plt.title('Validation Curves: Accuracy')\n",
        "plt.xlabel('Epoch'), plt.ylabel('Accuracy')\n",
        "plt.plot(history.history['accuracy'], label='training')\n",
        "plt.plot(history.history['val_accuracy'], label='validation')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "PtF-JPzC8oep"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# generate confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "Y_test_sequence_pred = model.predict(X_test_sequence)\n",
        "y_test_sequence_pred = np.argmax(Y_test_sequence_pred, axis = 1)\n",
        "conf_matrix = confusion_matrix(y_test_sequence, y_test_sequence_pred, labels=list(range(21)))\n",
        "\n",
        "# plot confusion matrix\n",
        "import seaborn as sn\n",
        "\n",
        "sn.set(font_scale=1.5) # for label size\n",
        "sn.heatmap(conf_matrix, fmt='.0f', annot=True, cmap='Blues')\n",
        "plt.ylabel('True Fault Class', fontsize=35)\n",
        "plt.xlabel('Predicted Fault Class', fontsize=35)"
      ],
      "metadata": {
        "id": "hdQWIvuc8rXL"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}